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One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
OneLife learns symbolic programmatic world models from a single episode of unguided exploration in stochastic environments
Zaid Khan, Archiki Prasad, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal
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One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation
a benchmark evaluating MLLMs’ ability to identify image rotation
Tianyi Niu, Jaemin Cho, Elias Stengel-Eskin, Mohit Bansal
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RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation
Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents
a unified framework that bridges multimodal LLMs and diffusion models with patch-level CLIP latents
Han Lin, Jaemin Cho, Amir Zadeh, Chuan Li, Mohit Bansal
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Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents
Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning
a framework that automatically constructs and leverages skill-aware CoT supervisions for domain-adaptive video reasoning
Daeun Lee*, Jaehong Yoon*, Jaemin Cho, Mohit Bansal
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Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning
CAPTURe: Evaluating Spatial Reasoning in Vision Language Models via Occluded Object Counting
a VLM benchmark testing spatial reasoning by making the models count objects under occlusion
Atin Pothiraj, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal
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CAPTURe: Evaluating Spatial Reasoning in Vision Language Models via Occluded Object Counting
Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model
A plug-and-play framework that reuses any existing ControlNet for any video/image diffusion model
Han Lin*, Jaemin Cho*, Abhay Zala, Mohit Bansal
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Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
A new testbed of teacher environments for data generation agents for diverse tasks.
Zaid Khan, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal
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DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding
Multi-modal RAG framework and dataset for multi-page multi-document understanding
Jaemin Cho, Debanjan Mahata, Ozan İrsoy, Yujie He, Mohit Bansal
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M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding
SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data
SELMA improves T2I models by fine-tuning on automatically generated multi-skill image-text datasets, with skill-specific LoRA expert learning & merging. - NeurIPS 2024
Jialu Li*, Jaemin Cho*, Yi-Lin Sung, Jaehong Yoon, Mohit Bansal
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SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data
DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning
Using LLM (GPT-4) to generate a ‘diagram plan’ for fine-grained layouts (object/text labels/arrows, etc.) and render in either raster images (via diffusion) and vector graphics (via PowerPoint / Inkscape or any tools). - COLM 2024
Abhay Zala, Han Lin, Jaemin Cho, Mohit Bansal
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DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning